“Ready to ship?” gets a thumbs-up emoji and a regret. A rollout risk review names the blast radius, defines the guardrail metrics, sets numeric abort criteria, and confirms support and comms are ready — before any percentage is bumped. These 12 prompts turn that review into a 10-minute checklist you run with Claude Opus 4.7, GPT-5.5, or Gemini 3.1 Pro instead of a gut-feel “looks fine to me.”
TL;DR
- Paste your diff, flag name, and dashboard URLs into the prompts below. The model returns a phased plan, abort thresholds, and a comms draft you can edit, not ship blind.
- Phased rollout that works in practice (as of June 2026): internal → 1% → 10% → 50% → 100%, with a guardrail metric and a numeric abort threshold at every step.
- Abort criteria must be exact numbers (error rate, conversion drop, ticket spike), not “if it looks bad.” Modern flag platforms like LaunchDarkly’s guarded rollouts will auto-pause or auto-roll-back when a monitored metric regresses — your prompt should output the thresholds those guardrails need.
- Best model for this review: a long-context reasoning model. All three flagships now carry a 1M-token window, so you can paste the full diff plus the runbook in one shot.
Who this is for
Engineering managers, founders, PMs co-owning launches, and any solo dev who has watched a 100% rollout turn into a 100% rollback. If you ship behind feature flags (LaunchDarkly, Statsig, Unleash, Flagsmith, ConfigCat, PostHog) or a homegrown toggle, these prompts apply.
When not to use these prompts
Skip the full review for a change hidden behind a paid setting that 10 customers use, or a copy-only tweak. Infra-only changes (deploys, schema migrations) need a different review — see the deployment and release-checklist prompts linked at the end. Use the full ramp for anything touching auth, payments, data writes, or visible behavior.
Which model to run these on
Any of the three June-2026 flagships handle this well; all three now ship a 1M-token context window, so the whole diff plus the runbook fits in one prompt.
| Model | Context | API in/out ($/1M) | Best fit for rollout review |
|---|---|---|---|
| Claude Opus 4.7 | 1M | $5 / $25 | Deepest reasoning on reversibility and blast radius; 87.6% SWE-bench Verified |
| GPT-5.5 (Thinking) | 1M (full only on $200 Pro) | $5 / $30 | Strong at terminal/ops checklists; 82.7% Terminal-Bench 2.0 |
| Gemini 3.1 Pro | 1M | $2 / $12 | Cheapest long-context pass over a large diff |
In a chat subscription, Claude Pro ($20/mo, $17 annual) or ChatGPT Plus ($20/mo) both run the review fine; the in-app context on Plus is ~320 pages, so paste the diff selectively. For a CLI-driven review against your real branch, Claude Code or Cursor can read the diff directly.
12 copy-ready prompt templates
Swap the backtick placeholders (`[featureName]`, `[old]`) for your real values. Each prompt names a role, the deliverable, and a hard constraint so the model doesn’t drift into vague advice.
1. Rollout risk triage
For feature `[featureName]`, assess: (1) Blast radius (% users / which segments), (2) Reversibility (instant flag flip / requires deploy / requires migration backtrack), (3) Detection lag (how long before we'd notice broken), (4) Support load impact. Output GREEN / YELLOW / RED with a one-line reason each.2. Phased rollout plan
Design a phased rollout: (1) Internal / employees only, (2) 1% real users 24h, (3) 10% 24h, (4) 50% 48h, (5) 100%. For each phase: guardrail metric, alert threshold, abort criteria, who decides to advance. Don't propose Day 1 = 100%.3. Abort criteria
Define abort criteria for this rollout: (1) Error rate threshold per endpoint, (2) Conversion drop threshold, (3) Support ticket spike threshold, (4) On-call paging count, (5) SLO error-budget burn rate. Specify exact numbers, not "if it looks bad". Include who has authority to abort.4. Cohort / targeting plan
Choose target cohorts for the first rollout: (1) Internal employees, (2) Power users (engaged 30+ days), (3) New signups (less harm if broken), (4) Specific orgs we trust. Avoid: paid enterprise, accessibility-flagged users. Output a cohort definition I can paste into a feature-flag targeting rule.5. A/B vs rollout decision
Should this be a rollout or an A/B test? Criteria: (a) Are we measuring uplift vs catching regressions? (b) Is sample size sufficient for an A/B? (c) Is the metric well-defined? (d) How long would we keep the variant arm alive? Recommend one, with reasoning.6. Pre-launch checklist
Generate a launch checklist: (1) Feature flag live in target env, (2) Dashboards exist with new metrics, (3) Guardrail metric + alert armed, (4) Rollback / kill-switch tested, (5) Support docs published, (6) Customer comms drafted, (7) On-call assigned. Each item GO / NO-GO with the missing owner.7. Support readiness
Audit support readiness: (1) Help-center article live, (2) Macro / canned reply created, (3) Internal Slack channel for escalation, (4) Known limitations documented. Output: missing items + owner + due date.8. Comms plan
Draft comms: (1) In-product release note, (2) Email to existing affected users (if behaviour changes), (3) One social post for one user-visible benefit, (4) Status-page entry if any risk of degradation. Keep each ≤ 100 words.9. Sunset rollout
We're sunsetting feature `[old]`. Plan: (1) Deprecation banner at T-30, (2) Email at T-14, (3) Read-only at T-7, (4) Full removal at T. Per phase: comms + opt-out / migration path. Customers must hear about it 3 times via different channels.10. Rollout post-mortem
Rollout `[featureName]` hit an abort criterion. Write a brief retro: (1) What broke, (2) Detection time, (3) Rollback time, (4) Why didn't an earlier phase catch it, (5) One process change. ≤ 250 words. No blame, just the system gap.11. Slow rollout for risk-averse customers
Some customers (enterprise / regulated) don't want auto-rollout. Design an opt-in path: (1) Default state, (2) UI to opt in, (3) How they roll back, (4) Comms cadence. Don't force changes on customers who told us not to.12. Re-rollout after rollback
We rolled back. Now we want to retry. Plan: (1) Root cause fixed + tested, (2) Phase plan restarted from 1%, (3) Anything we should monitor that we didn't before, (4) Comms — what to say to customers who saw the old version.How feature-flag platforms changed the math (June 2026)
The prompts above stay tool-agnostic, but the platform you ship on decides how much of the review you automate versus do by hand.
| Platform | Auto-rollback on regression | Pricing posture (as of June 2026) |
|---|---|---|
| LaunchDarkly | Yes — guarded rollouts auto-pause or auto-roll-back when a guardrail metric regresses (sequential testing) | Per-MAU/seat; enterprise quotes commonly $25K+/yr |
| Statsig | Yes — flag-to-experiment, sequential testing | Event-based; now part of Amplitude (post-OpenAI-acquisition) |
| Unleash / Flagsmith | Manual gates + gradual rollout; OSS self-host option | Free OSS tier; paid cloud |
| PostHog | Flags + experiments + analytics in one | Generous free tier, usage-based |
| ConfigCat | Percentage targeting, simple gates | Free tier; flat paid plans |
If your platform supports guarded rollouts, prompt #3 should produce the exact thresholds it needs. A common SRE trigger: auto-roll-back when the SLO error-budget burn rate exceeds 2x and remaining budget drops below 20% inside a 15-minute window. Feed that rule straight into your guardrail config.
Common mistakes
- Skipping a phased rollout because “the feature is small.” Small features still hit auth, payments, or data paths.
- No abort criteria — you keep advancing because nobody wants to be the one who stops it.
- Targeting paid customers first — high blast radius, high reputational cost.
- Forgetting support — they get blindsided and your CSAT tanks for a week.
- No reversibility check — you discover at 50% that a migration means you can’t go back.
- Conflating an A/B test with a rollout — they measure different things (uplift vs regressions).
- No comms — customers find out from a social post, not from you.
How to push results further
- Start at 1% real users. Less than that misses the signal.
- Abort criteria must be concrete numbers, not “if it feels bad.”
- Pair every success metric with a counter-metric (engagement up but support load up = abort).
- Test rollback before phase 1, not after phase 4.
- Comms = in-product + email + status page for anything user-visible.
- Sunset slowly: 3 touchpoints over 30 days.
- Run a retro after every aborted rollout — it usually reveals a monitoring gap, not a coding bug.
FAQ
- Does every feature need a phased rollout?: No. Small UI tweaks and copy changes don’t. Behaviour changes, and anything touching auth, payments, or data, do.
- What’s a good first cohort?: Employees → power users → new signups → general → enterprise. Lowest blast radius first.
- A/B test or rollout?: A/B when you’re measuring uplift; rollout when you’re catching regressions. They serve different goals and you can chain them (rollout to 10%, then A/B the variant).
- How fast should abort be?: Under 5 minutes for a code flag flip, under 30 minutes for anything data-backed — same expectation as a deploy.
- Can the AI set my guardrail thresholds for me?: It can draft sensible defaults from your past incident data, but treat them as a starting point. Tie them to your real SLOs and error budget, then let the flag platform enforce them.
- When can I retire a feature flag?: After ~30 days at 100% with no aborts. Otherwise the stale flag becomes its own debt — pick a flag type (release vs ops vs permission) so cleanup is built in.
Related
- Release checklist prompts
- Deployment check prompts
- Code review prompts
- PR review prompts
- Changelog Generation Prompts: 12 Templates for Useful Release Notes
- Coding & Developer Prompts hub
Tags: #Prompt #Coding #Release #Feature flag